REPLACEMENT SPARE PART INVENTORY MONITORING USING ADAPTIVE NEURO FUZZY INFERENCE SYSTEM

<p><strong>Abstract </strong></p> <p> </p> <p>The amount of inventory is determined on the basis of the demand. So that users can know the demand forecasts need to be done on the request. This study uses the data to implement a replacement parts on the electronic module production equipment in the telecommunications transmission systems, switching, access and power, ie by replacing the electronic module in the system is trouble or damaged parts of a good electronic module spare parts inventory, while the faulty electronic modules shipped to the Repair Center for repaired again, so that the results of these improvements can replenish spare part inventory.</p> <p>Parameters speed on improvement process of electronic module broken (repaired), in the form of an average repair time at the repair centers, in order to get back into the electronic module that is ready for used as spare parts in compliance with the safe supply inventory warehouse. This research using the method of Adaptive Neuro Fuzzy Inference System (ANFIS) in developing a decision support system for inventory control of spare parts available in Warehouse Inventory taking into account several parameters supporters, namely demand, improvement and fulfillment of spare parts and repair time.</p> <p>This study uses a recycling input parameter repair faulty electronic module of the customer to immediately replace the module in inventory warehouse, do improvements in the Repair Center. So the acceleration restoration factor is very influential as the input spare parts inventory supply in the warehouse and using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method.</p> <p> </p> <p>Keywords: ANFIS, inventory control, replacement